This modern period of science has been described as the post-genomic era, where all available sources of information are explored at the biological systems level. This is in stark contrast to the older genocentric view, which sought to describe simple links connecting individual genes to particular phenotypes. Considering then the many genes which may contribute to disease, it is understandable that modern attempts to associate phenotypes with genetic variations do so on a genomic scale, using interactome, especially in the case of complex diseases, such as cancer, tuberculosis, etc.

The present version implements two main objects, critical to post-analyses, network generator and semantic similarity tools:

Integrated Human Protein-Protein Interaction Network Generator: IHP-PING

Python library implementing Semantic Similarity Measures [Library]: PySML

All the post-analysis-tools functions and methods have been documented in a PDF manual, providing a more user-friendly starting point on admininstration and usage.

The post-analysis-tools development was initially written by Gaston K. Mazandu and collaborators. In addition, a large group of individuals is contributing to post-analysis-tools by sending suggestions and bug reports.

Since mid-2015 there has been a core group who worked on initial core of the post-analysis-tools code archive. This file has been revised since the release of the A-DaGO-Fun tool in 2015. The new releases and associated versions are provided below:

   - v2.4.1: The IHP-PING package.
   - v2.5.1: The PySML library.

These tools are freely downloadable under GNU General Public License (GPL), precompiled for Linux version and protected by copyright laws, a free software and comes with ABSOLUTELY NO WARRANTY.

Please use the pysml-dev or ihp-ping-dev specific web link to contact the maintainer for related suggestions or for reporting potential bugs and errors or specific concerns related to the post-analysis-tools related codes.

Any work dependent on open-source software owes debt to those who developed these tools. The authors thank everyone involved with free software, from the core developers to those who contributed to the documentation. Many thanks to the authors of the freely available libraries for making this work possible. This study is supported by the National Institutes of Health (NIH), USA, under Common Fund under H3ABioNet (U24HG006941) and SADaCC (1U01HG007459-01).